Computer-Implemented System and Method for Building an Implicit Music Recommendation

ABSTRACT

A computer-implemented system and method for building an implicit music recommendation is presented. Listening habits and selections of a recommender are monitored. A determination that the recommender has repeatedly listened to a particular music selection is made. A recommendation is generated for the repeated music selection. The recommendation is transmitted to one or more recipients on behalf of the recommender.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional patent application is a continuation of U.S. patentapplication Ser. No. 11/877,568, filed on Oct. 23, 2007, pending, whichclaims priority under 35 U.S.C. §119(e) to U.S. Provisional PatentApplication Ser. No. 60/933,356, filed Sep. 10, 2007, the disclosures ofwhich are incorporated by reference.

FIELD

This application relates in general to music recommendation and, inparticular, to a computer-implemented system and method for building animplicit music recommendation.

BACKGROUND

Digital media players provide an easily customized, and increasinglyubiquitous, personal environment for experiencing digital media. Digitalmedia players include units dedicated strictly to media playback,particularly digitally recorded music or video; hybrid units thatprovide media playback as part of a suite of functions, such as “smart”mobile telephones with integral media players; and virtual units thatare implemented wholly in software for execution on a personal computeror similar device. Other digital media players are possible.

A portable digital media player for personal music enjoyment isillustrative of the features sets offered. For example, a digital mediaplayer could be loaded with an entire music collection and a musiclistener could choose music to play based on title, artist, genre,composer, album, or other criteria. The listener could also create playlists thematically organized for exercise, commute, home, or otherpurpose. FIG. 1 is a functional block diagram showing a prior artpersonal music environment 10. A typical portable digital media player11 uses either solid state memory or hard disk storage to store acollection 12 of recorded digital media 13, and other data, such as auser profile. A user interface 14 provides tactile or voice controls 15to select and play the media 13, and a display or other indication 16that the media 13 is being played or viewed. The capabilities of theuser interface 14 can vary, depending upon the size and form factor ofthe player 11. A data interface 17 allows the player 11 to upload themedia 13 from external sources 18 and to synchronize data. The datainterface 17 provides wired or wireless interconnections to hostcomputers or network servers and can be data- or cellular-networkcapable. Other components are possible.

Conventionally, recorded digital media 13 must first be uploaded onto adigital media player 11 from an external source 18 before becomingavailable for selection or playback. Music packaged in physical form 19,such as cassette, LP record, or CD, must first be converted intocompatible digital format, frequently requiring playback equipment and apersonal computer. The proliferation of high bandwidth network access20, such as the Internet, and the adoption of digital encoding allowmedia 13 to be purchased online and shared electronically throughdigital media stores 21 and similar online enterprises. Personal musiccomposition software 22 provides a further source of media 13 for onlinesharing and critique.

Despite their conveniences, digital media players have theirlimitations. Discovering media beyond the scope of the stored mediaremains a process divorced from player usage. For instance, fellowaficionados must share their media recommendations by word of mouth orwritten message and the recipient must then separately find and reviewthe media, after which the media must still be uploaded before finallybecoming available. Moreover, musical tastes are notoriously subjectiveand dependent on mood, timing, locale, and other factors. Thus, a mediarecommendation recipient may not be open to suggestion at the time ofreceipt, thereby further alienating media discovery from player usage.As well, adding media encountered in the ambient environment to a storedcollection requires identifying and remembering enough information aboutthe media to identify the music or work, and later obtaining the media,which is a process generally removed in time, place, and circumstance.

Conventional approaches fail to adequately facilitate media discovery bydigital media player users. The iPod digital media player and iTunesclient software respectively sold and licensed by Apple Inc. providefull featured digital media playback, and digital media transfer andpurchase through an online music store. Media can only be shared if partof the same collection, which is accessed through the client software.In addition, a recently announced collaboration between StarbucksCorporation, Seattle, Wash., and Apple Inc., Cupertino, Calif., enablesa user of a Wi-Fi enabled iPod digital media player to find out thetitle of a song currently being played in a Starbucks café through thetap of a button, after which the song can be purchased and downloadeddirectly from an online music store. See, e.g., John Markoff, Apple CutsiPhone Price Ahead of Holidays, N.Y. TIMES, Sep. 6, 2007, the disclosureof which is incorporated by reference. However, the service onlyoperates in participating stores with subscribed iPod players and doesnot automatically integrate the song title into an existing musiccollection, absent purchase and download.

The Zune digital audio player and client software respectively sold andlicensed by Microsoft Corporation, Redmond, Wash., provide limitedwireless file sharing, and digital music purchase through an onlinemusic store. Stored digital audio can be shared between Zune users undera “three plays or three days, whichever comes first” policy. The policyis indiscriminately applied to any audio content transferred betweenplayers and shared songs expire unconditionally in three days, even ifnot played. File sharing is limited to other Zune users within physicalrange, and email, messaging, and recommendations from sources that areout of range remain unavailable. As a result, file sharing remainsunappealing.

The MusicGremlin Portable Wi-Fi Device sold by MusicGremlin, Inc., NewYork, N.Y., provides a portable music player for use with a musicsubscription service, which allows a user to wirelessly search, play,and download music using the device. Programmed playlists and musicpurchased through a client personal computer can also be automaticallydownloaded to the device, and music recommendations can be exchangedwith fellow subscribers. However, the device is tied to a specificsubscription service. As well, the music recommendations are notcontextually integrated into the device's playlist and lack ratings,which would help a recipient to evaluate their overall utility in lightof other recommendations and sources.

A metadata sharing application for mobile phones is described in S.Baumann et al., “BluetunA: Let Your Neighbour Know What Music You Like,”CHI 2007 (Apr. 28-May 3, 2007), the disclosure of which is incorporatedby reference. Users of Bluetooth-enabled mobile phones can shareinformation about their music preferences by allowing other users inproximity of their mobile phone to access information about their playlist. However, the information sharing is limited by physical devicerange and works anonymously, thereby providing information untied tocredibility or authoritativeness of the source.

Finally, online music services, such as Rhapsody, licensed byRealNetworks, Seattle, Wash., and Last.FM, licensed by Last.fm Ltd.,London, UK, provide personalized music recommendations. Rhapsody offersa subscription music listening service that can provide personal musicrecommendations. Last.FM builds a profile of a user's musical tastesbased on his listening habits as monitored from streamed radio stationsor digital music player. The music recommendations, though, are basedupon the user's own musical tastes and not evolved from externalsources, such as through a social network.

Therefore, there is a need for integrating music recommendation anddiscovery into a personal listening environment, both in easing themanner of making a music recommendation and in presenting therecommendation to a music listener when most receptive.

SUMMARY

One embodiment provides a digital media player and method forfacilitating music recommendation. A music recommendation is built for arecommender. A recipient for electronic receipt is named. Music to berecommended is identified. Information to describe one or more of anidentification of the music to be recommended, the recommender, acontext of the recommendation, and a rating for the music to berecommended is added. The music recommendation is provided to therecipient.

A further embodiment provides a computer-implemented system and methodfor building an implicit music recommendation. Listening habits andmusic selections of a recommender are monitored. A determination thatthe recommender has repeatedly listened to a particular music selectionis made. A recommendation is generated for the repeated music selection.The recommendation is transmitted to one or more recipients on behalf ofthe recommender.

Accordingly, from a user's point of view, the continuum of actions fromrecommending to receiving recommendations, and of music logging become aconvenient and integrated procedure that helps make music discovery anelegant and pleasant experience. From the perspective of musicdistributors, social networks become effective viral marketing channelsfor the “long tail” of less popular music selections.

In addition, recommendations from friends and other sources areintegrated into a personal music collection in context. A user is thusable to locate new music for which there is good reason to believe thatthe user would be interested in listening. Moreover, the integrationplaces the recommendations at a place where he would be likely toencounter the recommendations, particularly while browsing his ownstored music. Music in his collection is thereby combined with othermusic in which he would likely have an interest.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein are described embodiments by way of illustratingthe best mode contemplated for carrying out the invention. As will berealized, the invention is capable of other and different embodimentsand its several details are capable of modifications in various obviousrespects, all without departing from the spirit and the scope of thepresent invention. Accordingly, the drawings and detailed descriptionare to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing a prior art personal musicenvironment.

FIG. 2 is a functional block diagram showing a system for facilitatingmusic recommendation, in accordance with one embodiment.

FIG. 3 is a functional block diagram showing a system for facilitatingsocial music discovery through sampling, identification, and logging.

FIG. 4 is a data flow diagram showing, by way of example, components ofa music recommendation.

FIG. 5 is a data flow diagram showing, by way of example, sources ofpersonal music.

FIG. 6 is a process flow diagram showing a method for facilitating musicrecommendation, in accordance with one embodiment.

FIG. 7 is a graph showing, by way of example, receptiveness tomusic-selection suggestion as a function of music activity.

FIG. 8 is a screen diagram showing, by way of example, a visual displayfor a personal music collection for use with the systems of FIGS. 2 and3.

FIG. 9 is a process flow diagram showing music suggestion placement in avisual display for use with the methods of FIGS. 2 and 3.

DETAILED DESCRIPTION

Although described here in relation to digital music, the embodimentsapply generally to all forms of digital media recordings, includingaudio and video recordings, as well as written information, such as newspostings and Web pages. Additionally, digital media players embrace allforms of digital media playback device, including portable, mobile, andstationary players.

Music Recommendation and Presentation Overview

Music can be efficiently recommended by one user of a digital mediaplayer to another user, even if the recommender and recipient users areseparated by time, place, or circumstance. FIG. 2 is a functional blockdiagram showing a system for facilitating music recommendation 30, inaccordance with one embodiment. By way of example, two individuals 31,37 are engaged in some fashion with their respective digital mediaplayers 33, 38, at the same or possibly different times. One of theindividuals is a music recommender 31, while the other individual is amusic recommendation recipient 37. The roles of recommender andrecipient are interchangeable and can involve more than a single pair ofmusic aficionados or digital media players.

The recommender 31 can decide to send a music recommendation 35(operation 32) to the recipient 37 at any time while listening to orviewing the music collection stored on his player 33. The recommendation35 could be for a limited excerpt, single song, selection of songs or“tracks,” album, or music compilation. The recommender 31 could also beusing his player 33 in some way other than listening to or perusing hisstored music collection. For instance, if the player 33 is integral to a“smart” mobile telephone, the recommender 31 may decide upon arecommendation 35 while on a call. The recommender 31 picks the musicand his player 33 generates the recommendation 35 (operation 34), eitherautomatically or with assistance from the recommender 31. The contentsof the recommendation 35 are further described below with reference toFIG. 4. In a further embodiment, the music can be digitally encoded toenable subsequent tracking, unless tracking is disallowed by the source,recommender, recipient, or other party. Once generated, the player 33electronically sends the recommendation 35 (operation 36) to therecipient's player 38.

The methodology of music recommendation (operation 34) is furtherdiscussed below with reference to FIG. 6. Briefly, unless therecommender 31 indicates otherwise, the player 33 automaticallyidentifies the song currently in play or being viewed as the music to berecommended. The recommender 31 must specify a recipient 37 and theplayer 33 generates a music recommendation 35 that is subsequentlytransmitted to the recipient 37. In a further embodiment, the musicrecommendation 35 and details regarding its use (not shown) can besubsequently tracked, unless tracking is disallowed by the recommender31, recipient 37, or other party.

The recommendation 35 is received by the recipient's player 38 forpresentation to the recipient (operation 39). Upon learning of thereceipt of the recommendation 35 from his player 38, the recipient 37can immediately listen to or view the recommendation 35, save therecommendation 35 for later presentation, or ignore or discard therecommendation 35 altogether. Absent other disposition, therecommendation 35 is automatically integrated into the recipient's musiccollection, such as described in commonly-assigned U.S. Pat. No.8,340,796, issued Dec. 25, 2012, the disclosure of which is incorporatedby reference. The music title or other information from therecommendation 35 appears as a virtual part of the recipient's musiccollection.

In a further embodiment, the music content itself may have to beseparately obtained. For instance, the player 38 may fetch and cache asample of recommended music, or even the entire digital work, as furtherdescribed below with reference to FIG. 6. By caching the recommendedmusic in the background, the player 38 can make music exploration moreresponsive and enjoyable, so that a user does not have to wait for musicto download before listening. In addition, the playing of the digitalwork may be subject to controls, such as digital rights management. Thedigital rights may allow the user to play the music once or to play asample of the music. The digital rights could have other limitations.Finally, the player 38 may facilitate a commercial transaction, so thatthe user can easily purchase the music or obtain the music from asubscription service.

Music Logging Overview

Music can be efficiently discovered through logging by a digital mediaplayer, even if the viewing or playing of the music on the player isseparated by time, place, or circumstance. FIG. 3 is a functional blockdiagram showing a system for facilitating social music discovery throughsampling, identification, and logging 40. Music logging can occur ondemand upon the action of an individual music “discoverer” 41, orautonomously by automatic music logging performed by his digital mediaplayer 41.

The discoverer 41 could be engaged in some fashion with his digitalmedia player 41. Upon hearing music playing in the ambient environment42, the discoverer 41 decides to take a sample, that is, a shortrecording, of the music (operation 44). In a further embodiment, theplayer 43 automatically senses and samples the music without any furtheraction by the discoverer 41. The music sample is thereafter identified,if possible, although the discoverer 41 may need to be prompted tochoose a particular version if several known versions of the music arefound. Following identification, the music is integrated into thediscoverer's music collection, such as described in commonly-assignedU.S. Pat. No. 8,060,227, issued Nov. 15, 2011, the disclosure of whichis incorporated by reference. In a further embodiment, the music samplecan be digitally encoded to enable subsequent tracking, unless trackingis disallowed by the source, recommender, recipient, or other party.

Music Recommendation Components

A music recommendation contains information about the music that isbeing recommended. The music recommendation does not necessarily includethe music itself, which could be separately obtained and uploaded by therecipient. FIG. 4 is a data flow diagram showing, by way of example,components 50 of a music recommendation 51. The components 50 aregrouped into information relating to identification of the music 52, therecommender 53, the context 54 of the recommendation 51, and theassociated rating 55. Other information components are possible.

Identification information 52 relates to information about the music ordigital work itself. For clarity, the music that is being recommendedwill hereafter be referred to as a music selection, although the musiccould be a single tune or song, selection or related set of songs or“tracks,” album, music compilation, or other type or form of related orassociated music content. Specific identification information 52 caninclude the title of the music selection, performing artist, album name,genre, publisher, availability, length, digital identifier, and a sampleof the music or work. Other specific identification information ispossible.

The recommender information 53 identifies the source of therecommendation 51. Absent consent, personal privacy considerations maylimit what information is provided for an individual that sent arecommendation, although an institutional recommender, such as arecommendation service or online store, might be willing or required todivulge their information. Specific recommender information 53 includesthe recommender's name, relation, such as friend, subscribed, orautomated system, address data, phone number, email address, and Website. Automated system information includes data on usage, socialfiltering, or collaborative filtering. In addition, informationconcerning a self-recommendation, which must include an explicit act bythe user to rate the music highly, as further described below withreference to FIG. 5, can include whether the information originates froma subscription or news service, or a sampling source. Other specificrecommender information is possible, including text or voiceannotations.

The context information 54 relates to the time, place, or circumstanceof a recommendation. The specific context information available willdepend upon the setting or environment in which the recommendation wasmade. For instance, the time and date of a recommendation can generallybe discerned based on when the recommendation was sent, even if therecommender's digital media player lacks a built-in clock. However, thecircumstances surrounding a recommendation may not be directly known,unless the recommender annotates the circumstances to therecommendation. Other context information would have to be eitherdirectly sensed by the digital media player, or indirectly derived.Thus, a geolocational receiver would be needed to automaticallydetermine location if the digital media player is portable. Similarly, acalendar would have to be evaluated to match up events torecommendations, such as a concert, party, or holiday. Other specificcontext information is possible.

The rating information 55 is typically based on a Leikert scale. Therating could two-level or multi-level. The rating is generally from therecommender. Other specific rating information is possible.

Recommendation Sources

Over time, a digital media player can amass a wide assortment of music,which has been received from various sources. FIG. 5 is a data flowdiagram showing, by way of example, sources 60 of personal music. Thesources 60 can be loosely grouped into “traditional” sources 62,recommendation sources 63, and sampled sources 69. The sources aremerely representative of common music sources available to a musiccollector. Other music sources are possible.

Traditional sources 62 include storefront and online music retailchannels, such as described above with reference to FIG. 1. However,this source places the burden of growing the personal music collection61 on the collector, relying on his efforts and intuition to buy orobtain music as activities collateral to the use and enjoyment of hisdigital media player. The collector is left to seek out and auditionmusic on his own before taking further steps to purchase or obtain,convert, upload, and integrate the music into his collection 61.

Recommendation sources 63 include several types of recommenders. Adirect recommendation source 64 is a form of person-to-personrecommendation and includes recommendations received from friends,family, colleagues, or fellow aficionados, such as described above withreference to FIG. 2. A direct recommendation source 64 is also known associal filtering or social recommendations. A recommendation originatingfrom an online recommendation service or individual recommender, such asan online music critic that sends a music recommendation each week,constitutes a subscribed recommendation source 65. The user of theplayer can also be a self-recommendation source 66 for music or otherdigital media originating from an ambient source. The user signalsapproval, such as by tapping his player, that an ambient sourceidentified by the player should be sampled for music logging. Ausage-based recommendation source 67 is similar to a direct source 64,except by functioning implicitly to recommend music or other digitalmedia by tracking listening habits and automatically generating arecommendation when user profile usage recommendation parameters aresatisfied. Finally, a popularity- and profile-based recommendationsource 68 bases recommendations on groups of users who appear to sharecommon tastes. A recommendation is generated if group members buy orlisten to certain music. A popularity and profile recommendation source68 is also known as collaborative filtering or collaborativerecommendations. Other types of recommendation sources are possible.

The quality or authoritativeness of the recommendation source matters.Recommendations from trusted sources are considered more reliable thansources that generally send irrelevant or uninteresting recommendations.The recommender information 53 and context information 54 included aspart of each recommendation 51 (shown in FIG. 4) can help a collector toorganize or filter recommendations, as well as the recommendersthemselves, as further described below with reference to FIG. 9. Forinstance, reliable recommenders whose recommendations are generallytrusted and followed may be rated higher or assigned greater weight thanother recommenders. In addition, the recommendations from those trustedrecommenders could also be displayed more prominently to reflect theirhigher credibility or greater weight. Conversely, recommendations fromseldom-followed or untrusted recommenders could be automatically ignoredor discarded.

Finally, sampled sources 69 include music recommended by the userhimself and samples autonomously collected from an ambient environment.Like recommendation sources 63, sampled sources 69 can be evaluated andrated. Raw samples from random music logging might be the lowest rated,as the music would likely be of lowest interest. Self-recommended music,though, might be highly rated, particularly where the music selectionsampled can be completely identified from the music sample alone. Forexample, if music logging takes place “on demand,” the act of taking thesample reflects the user's intention and implies a high rating. If musiclogging is proactive, that is, autonomous, the user could signal arating while the music is playing, or could add a rating later whilelistening to the music. Other types of sampled sources are possible.

Recommendation

Music recommending refers to the selection and sending of arecommendation of one or more music selections by a digital mediaplayer. FIG. 6 is a process flow diagram showing a method 70 forfacilitating music recommendation, in accordance with one embodiment.The method is performed as a series of process steps or operationsexecuted by a digital media player or similar device.

Each music recommendation first begins with the selection of one or moremusic selections via a digital media player (operation 71). Theselection can be an explicit recommendation made by the recommender. Theselection could instead be an implicit recommendation generated by awatcher program executing on the player that monitors the listeninghabits and selections of the recommender in the background. For example,the player could create a recommendation upon sensing that therecommender has repeatedly listened to a particular music selection. Theplayer would then automatically generate a recommendation on behalf ofthe recommender for those recipients to whom similar recommendationshave been sent in the past. The player is itself the “recommender,”although acting with the tacit permission of the user. Alternatively,the recommender might be prompted by his player to definitively approvethe selection. Thus, the recommendation reflects the recommender's levelof intention, where an explicit recommendation reflects a strong levelof intention, an wholly implicit recommendation reflects a weak level ofintention, and an implicit recommendation subject to recommenderapproval reflects a medium level of intention.

In addition, the recommendation need not include the actual music oreven constitute a complete musical work. Rather, the recommendationitself could contain no music whatsoever, a rendition with initiallimitations on playback rights, such as a music sample or play-limitedversion, a hyperlink or reference to an online sample, anotherrecommendation that is being forwarded from another recommender, or evena narrative or annotation dictated by the recommender. Other content ispossible.

In simplest form, the recommender only has to push a single “Recommend”button on his player to make the recommendation. The “Recommend” buttonis analogous to a “Call” button on a mobile telephone. Alternatively,the recommender could explicitly choose the selection, such as bypicking a music selection from a display. In a further embodiment, theuser interface of the player could provide a range of means to specify arecommendation. For instance, the player could include a motion sensoror accelerometer that accepted physical gestures as inputs, such as ashake, tap, or other movement, which would each signify the choice of alimited excerpt, single song, album, or other selection. The playercould also provide voice recognition by which the recommender couldspeak out the music selection and recipient.

Upon identifying the music selection to be recommended, the recommendermust specify at least one recipient (operation 72), which could be byname, email address, phone number, or other identification. Therecipient and music selection constitute a starting point or “shell” ofa music recommendation.

The player also adds as much recommendation information 51 that can beascertained, as further described above with reference to FIG. 4. Inaddition, the recommender can optionally annotate a message to therecommendation (operation 73), either in text or voice, where supported.In a further embodiment, the player could automatically pick a recipientbased on a list of frequently selected recipients or other criteria,such as shared musical interests or history of past recommendations andwhether the recipient followed them.

The player then electronically sends the recommendation (operation 74).The recommendation can be sent immediately, if the player has thecapability, such as via wireless or cellular network. Alternatively, thesending of the recommendation can be deferred until the player isconnected or “docked” to a synchronization device, such as a personalcomputer.

Following sending, the recommendation can be tracked (operation 75),unless tracking is disallowed by the recommender, recipient, or otherparty. Tracking can include maintaining an internal record of eachrecommendation, plus recipient list, although other tracking informationmay also be maintained, such as disposition, for instance, “In Transit,”“Read,” “Unread,” “Discarded,” or “Purchased.” Other trackingdispositions are possible. The tracking information can be maintained onthe recommender's player or other location, such as a centralizedserver, although the recipient's player would have to allow the trackinginformation to be provided back to the recommender. The trackinginformation would thereafter be accessible by the recommender, or otherauthorized party, and could be used to decide whether subsequentrecommendations to a recipient would likely be followed, or otherdetermination.

Finally, in a further embodiment, the recommender may receive anincentive from a music retail channel (operation 76) in recognition ofhaving facilitated the eventual purchase of a recommended musicselection. The tracking information would need to be made available tothe music retail channel, which would need to corroborate therecipient's purchase to safeguard against fabricated claims. Still otherrecommendation operations are possible.

The improved ease of use and convenience afforded in musicrecommendation through the foregoing method is best illustrated throughthe following scenario. Suppose a listener is exercising at a gym and islistening to a tune on his digital media player. He realizes that hisson might be interested in the tune to which he is presently listening.To send a music recommendation, he taps a button on his digital mediaplayer and speak a message into his player, such as “Message for Morgan.Check out this piece, The River Sings, by Enya, which reminds me ofOrinoco Flow.” The approach is analogous to “hands-free” calling onmobile telephones, which facilitate dialup to any party listed in adigital address book. Here, the user interaction is extremely easy forthe listener because Morgan, his son, is already in his music-relatedaddress book and the player automatically defaults to recommend the tunethat is currently being played. Thus, the listener need not speciallyselect Morgan's name from a list, specify a music selection, or thinkabout formats. Rather, the listener merely need push a button, speak amessage, and resume with his workout. The digital media playerautomatically annotates his message to the music recommendation anddispatches the recommendation to Morgan's digital music player.

User Receptiveness

Logically integrating the recommended music selections into the musiccollection helps to opportunistically suggest the potential purchase ofmusic to the recipient 25 at a time when he may be most receptive. FIG.7 is a graph 80 showing, by way of example, receptiveness tomusic-selection suggestion as a function of music activity. The x-axis81 represents music-related activities as a continuum of personalinvolvement on the part of the music listener. The y-axis 82 representsthe relative level of receptiveness experienced by a music listener inincreasing order.

Receptiveness 83 can be diagrammatically depicted as a function ofmusic-related activities. Minimal activity 84 occurs when the user ishaving little to no involvement with his player. Searching activity 85is goal-oriented and occurs when the user is trying to find a particularselection of music, which is also when the user would likely welcomerelevant recommendations. The player enlarges the search scope toinclude the recommendation information, as described above withreference to FIG. 4. Organizing activity 86 occurs after music has beenfound, but prior to listening, such as while composing a play list.During organizing activity 86, the available selections of music havebeen narrowed down by taste or other criteria and recommendations onlyappear within the display parameters in force. Finally, listeningactivity 87 connotes the actual playing of music on the player. Theshaded area 88 of the receptiveness curve 83 respectively bounded by thesearching and organizing activities 85, 86 represents peakreceptiveness, during which times the listener is most likely to bereceptive to a recommendation. Integrating the music recommendationsinto the display thereby ensures that the recommendations are availableboth in form with existing music for purposes of organization andthrough recommendation information for location through search.

Visual Display

Limits on human visual perception and digital manipulative abilitiesrestrict the physical sizes of the displays and controls provided in theuser interfaces of digital media players. FIG. 8 is a screen diagramshowing, by way of example, a visual display 90 for a personal musiccollection for use with the systems of FIGS. 2 and 3. The visual display90 assumes sufficient space to provide multiple rows of informationregarding a personal music collection. The display 90 is supplementedwith controls (not shown) to enable navigation and selection of music,although the composition of play lists is generally performed offline,where text entry capabilities are available. The display 90 includesparameters 91 to display music selections by artist 92, title 93, album94, genre 95, and source 96, although other display parameters arepossible. Each column is sortable and searchable, as accommodated by thecontrols.

Music selections, both recommended and sample music that has beenidentified, are included in the visual display as an integral part ofthe music collection. In general, the amount of information displayed issensitive to the amount of space available and allocated in the display.For example, on a player in which a user can “open” or “close” thefolders for groups of music shown in a display, opening a Celtic folderwould show all artists and albums in the Celtic genre. Closing the samefolder might just show the genre, Celtic, and not the music containedwithin. The display of recommendations thus follows the display of themusic. If all music selections are displayed, recommendations in theCeltic genre would show up closest to the most relevant selections.However, if the folder is closed, the player would only show the numbersof music selections and recommendations. In addition, the display canincorporate further useful elements. When a user is looking at arecommendation, controls could enable him to read or listen to anymessage from the recommender. The display could also show when therecommendation was made and any rating provided by the recommender. Ifthere is more than one recommendation for a music selection, the usermay want to read or listen to multiple recommendations. Finally,controls could facilitate buying, such as from an online music retailer,or otherwise obtaining the recommended music, for instance, from asubscription service.

The type of music selection, that is, existing music, recommended music,and sampled music, can be indicated using a different font, color, orother display attribute. In addition, the quality or authoritativenessof the recommender can be applied, either in how the music selectionappears or whether the music selection is rated or omitted. Moreover,the “staleness” of a music selection could affect manifestation in thevisual display. For instance, a recommendation or sample that issignificantly older than more recent music selections might be filteredout. Other visual display criteria are possible.

In a further embodiment, the spatial arrangement of the integrated musicselections can be controlled to help distinguish recommended or sampledmusic from already-existing music acquisitions. Various indications canbe used, including representing each piece as an icon or symbol, placingthe recommended pieces adjacent to the music collection, or ordering andhighlighting the recommended pieces within the music collection. Inaddition, a calendar could be generated to match up events torecommendations, such as a concert, party, or holiday, or a timelinecould be created with one or more clusters of music recommendations.Still other spatial arrangement or indications are possible.

Music Suggestion Placement

The recommended and sampled music selections are logically integratedinto the visual display in an ordering to help ensure relevant anduseful placement. FIG. 9 is a process flow diagram showing musicsuggestion placement in a visual display for use with the methods ofFIGS. 2 and 3. The ordering determines placement as a function of degreeof interest and contextual relevance.

First, the degree of interest 101 rates whether a recommendation isexpected to be of interest to the listener in any context. The degree ofinterest 101 depends upon recommendation-specific factors, such therecommendation source and associated reliability, described above withreference to FIG. 5; the recommender's level of intention, describedabove with reference to FIG. 6; the reliability of the recommender; andtiming, that is, when the recommendation was sent. A recommender whoserecommendations are generally followed, such as a subscribedrecommendation source, would be assigned more weight than aseldom-followed recommender, like a collaborative recommendation.Similarly, a recommendation recently sent would be afforded greaterweight than a stale recommendation. Other factors weighing into degreeof interest 101 are possible.

Second, the contextual relevance 102 characterizes the current listeningcontext of the listener and can include evaluation of the possiblespectrum of music-related activities 81, as described above withreference to FIG. 7. Contextual relevance seeks to identify thoserecommendations to which a listener would presently be most receptive.For example, a listener browsing “Baroque music” would not likely havemuch interest in contemporary pop or light jazz. Contextual relevancecan be derived, for instance, from the display, which generally isconstrained to show only a subset of an overall personal musiccollection. Thus, contextual relevance applies similar constraints, suchas genre, artist, and so forth, in evaluating the recommendations toshow. Other types of contextual relevance are possible.

Finally, placement 103 is a function of the degree of interest andcontextual relevance 102. A music selection from a recommendation thatreflects a sufficient degree of interest and which is contextuallyrelevant may be placed or integrated into the collection. Actualplacement 103 applies the same constraints as listed music, and thusdepends upon the ordering principles of the listing and the relatednessof the recommended music to the displayed music. Other forms ofplacement are possible.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope.

What is claimed is:
 1. A computer-implemented system for building animplicit music recommendation, comprising: a watcher to monitorlistening habits and selections of a recommender; a determination moduleto determine that the recommender has repeatedly listened to aparticular music selection; a recommendation generator to generate arecommendation for the repeated music selection; and a delivery moduleto transmit, on behalf of the recommender, the recommendation to one ormore recipients.
 2. A system according to claim 1, further comprising: arecipient determination module to determine the recipients of therecommendation, comprising: a historical recipient module to identifyprevious recipients of other recommendations by the recommender; arecommendation comparison module to determine the other recommendationsthat are similar to the recommendation; and a recipient identificationmodule to identify the previous recipients who received the similarother recommendations as the recipients.
 3. A system according to claim1, further comprising: a request module to request approval of therecommendation from the recommender; a receipt module to receive theapproval of the recommendation; and the delivery module to send therecommendation upon receipt of the approval.
 4. A system according toclaim 1, further comprising: a tracking module to track therecommendation upon transmission to the recipients, comprising at leastone of: a status assignment module to assign a status to therecommendation, wherein the status comprises one or more of “intransit,” “read,” “unread,” “discarded” and “purchased,” and maintaininga list of statuses for the recommendation; and a list module to maintaina recipient list for the recommendation.
 5. A system according to claim1, further comprising: an annotation module to receive from therecommender, an annotation for the recommendation and to associate theannotation with the recommendation.
 6. A system according to claim 1,further comprising: an instruction receipt module to determine atrustworthiness of the recommender; and a display module to display therecommendation to the recipient based on a level of the trustworthiness.7. A system according to claim 6, further comprising: a recommendationdiscard module to discard the recommendation when the trustworthiness isdetermined to be untrusted.
 8. A system according to claim 1, furthercomprising: a recommendation matching module to match the recommendationwith an upcoming event; and the delivery module to provide the eventwith the recommendation.
 9. A system according to claim 1, furthercomprising: a recommender data module to associate information about therecommender with the recommendation.
 10. A system according to claim 1,wherein the recommendation comprises at least one of a song title,limited excerpt of a song, single song, selection of songs, album, andmusic compilation.
 11. A computer-implemented method for building animplicit music recommendation, comprising: monitoring listening habitsand selections of a recommender; determining that the recommender hasrepeatedly listened to a particular music selection; generating arecommendation for the repeated music selection; and transmitting, onbehalf of the recommender, the recommendation to one or more recipients.12. A method according to claim 11, further comprising: determining therecipients of the recommendation, comprising: identifying previousrecipients of other recommendations by the recommender; determiningwhether the other recommendations are similar to the recommendation; andidentifying those previous recipients who received the similar otherrecommendations as the recipients.
 13. A method according to claim 11,further comprising: requesting approval of the recommendation from therecommender; receiving the approval of the recommendation; and sendingthe recommendation upon receipt of the approval.
 14. A method accordingto claim 11, further comprising: tracking the recommendation upontransmission to the recipients, comprising at least one of: assigning astatus to the recommendation, wherein the status comprises one or moreof “in transit,” “read,” “unread,” “discarded” and “purchased,” andmaintaining a list of statuses for the recommendation; and maintaining arecipient list for the recommendation.
 15. A method according to claim11, further comprising: receiving from the recommender, an annotationfor the recommendation; and associating the annotation with therecommendation.
 16. A method according to claim 11, further comprising:determining a trustworthiness of the recommender; and displaying therecommendation within a display of the recipient based on a level of thetrustworthiness.
 17. A method according to claim 16, further comprising:discarding the recommendation when the trustworthiness is determined tobe untrusted.
 18. A method according to claim 11, further comprising:matching the recommendation with an upcoming event; and providing theevent with the recommendation.
 19. A method according to claim 11,further comprising: associating information about the recommender withthe recommendation.
 20. A method according to claim 11, wherein therecommendation comprises at least one of a song title, limited excerptof a song, single song, selection of songs, album, and musiccompilation.